class_docstr: A subclass to allow more customization of the Word2Vec internals.
code: def __init__(self, vocabulary_counts=None, **kwargs):
      self.vocabulary_counts = None
  
      kwargs["min_count"] = kwargs.get("min_count", 0)
      kwargs["workers"] = kwargs.get("workers", cpu_count())
      kwargs["size"] = kwargs.get("size", 128)
      kwargs["sentences"] = kwargs.get("sentences", None)
      kwargs["window"] = kwargs.get("window", 10)
      kwargs["sg"] = 1
      kwargs["hs"] = 1
  
      if vocabulary_counts != None:
          self.vocabulary_counts = vocabulary_counts
  
      super(Skipgram, self).__init__(**kwargs)
  
file_path: deepwalk/deepwalk/skipgram.py
imports: from collections import Counter, Mapping
  from concurrent.futures import ProcessPoolExecutor
  import logging
  from multiprocessing import cpu_count
  from six import string_types
  from gensim.models import Word2Vec
  from gensim.models.word2vec import Vocab
label_desc: Use image node_id 1 to create a new Skipgram object from inherited base classes: Word2Vec with example: obj = Skipgram(vocabulary_counts)